With the proliferation of audio/visual (A/V) content for consumption via consumer electronics (CE) devices, consumers can benefit from playlists for access to such content. As such, in many conventional media players, users are given the option of manually creating their own playlists based on the available media collection. However, because often numerous content items are involved, the manual creation of playlists consumes large quantities of time, which may need to be repeated for updating such playlists.
In another conventional approach, a playlist is generated based on an observed play pattern of certain content items by a user, indicating user interest in the content items. Parameters that indicate interest are a play count for a content item over a time period and a skip count indicating that a user is skipping a particular content item. While the first parameter adds positively to the popularity of a particular content item and type, the second parameter has a negative effect on its popularity. However, such playlists are created without considering that a user may have interest in other types of content.
In another conventional approach, a system generates playlists based on the interests of similar users. It is assumed that if a user is similar to some other users, that user is likely to like content the other users like. However, this approach requires knowledge of other users, and collecting information about their preferences.
Yet in another conventional approach, the system generates playlists by comparing genre, artist and other characteristics for a content item, to that of other content items in order to find similar content. However, only playlists for similar content items are generated, and other content items that the user may have interest in are not determined and are not included in such playlists.